Search

Restricted by the diversity of resources and the complexity of search algorithms,current search mechanisms in peer-to-peer file sharing systems are based on file names and simple keyword matching.These mechanisms cannot recognize deeper relationships between keywords and resources;hence it cannot provide high search quality.This paper proposes a new search scheme,which is built on top of the current peer-to-peer network. It harnesses users' search behaviors and download behaviors to automatically discover the deeper relationships between keywords and resources,which is then used to improve the search quality.It has the advantages of low implementation cost,low complexity,self-evolving,and supports for semantic search.Simulations based on the Maze system show that this approach has high search hit rate and accuracy.

The availability of a P2P (peer-to-peer) file sharing system is heavily affected by the high churn rate of users. When the largest P2P file sharing system Maze in CERNET is developed, the log collected in the system is used to get a better understanding of the users' characteristics, to find the key factor which influences the resource availability, and to instruct the future development of Maze system. In this paper, the concept of P2P file sharing systems' availability is redefined from users' perspective. With the log of Maze, it is the first study to use clustering technique to quantitatively categorize users in a P2P file sharing system. Based on the thorough study of behavior of the active users amounting to 0.77% of the total users and the impact on Maze's availability, this paper concludes that this kind of users plays a similar role to server so that they greatly increase the availability of system and are